






'''simple docstring'''







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam




class   __SCREAMING_SNAKE_CASE       (tf.keras.optimizers.schedules.LearningRateSchedule       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = 1.0   ,	UpperCamelCase__ = None   ,	):






         """simple docstring"""


         super().__init__()
         a_						     =			initial_learning_rate
         a_						     =			warmup_steps
         a_						     =			power
         a_						     =			decay_schedule_fn
         a_						     =			name







  def __call__(			self   ,	UpperCamelCase__	):






         """simple docstring"""


         with tf.name_scope(self.name or 'WarmUp'	) as name:
                # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
                # learning rate will be `global_step/num_warmup_steps * init_lr`.
                a_						     =			tf.cast(UpperCamelCase__   ,	tf.floataa	)
                a_						     =			tf.cast(self.warmup_steps   ,	tf.floataa	)
                a_						     =			global_step_float / warmup_steps_float
                a_						     =			self.initial_learning_rate * tf.math.pow(UpperCamelCase__   ,	self.power	)
                return tf.cond(
                    global_step_float < warmup_steps_float   ,	lambda: warmup_learning_rate   ,	lambda: self.decay_schedule_fn(step - self.warmup_steps	)   ,	name=UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         return {
             "initial_learning_rate": self.initial_learning_rate,
             "decay_schedule_fn": self.decay_schedule_fn,
             "warmup_steps": self.warmup_steps,
             "power": self.power,
             "name": self.name,
         }





def       __UpperCamelCase					(      lowercase_       :  float   ,       lowercase_       :  int   ,       lowercase_       :  int   ,       lowercase_       :  float = 0.0   ,       lowercase_       :  float = 0.9   ,       lowercase_       :  float = 0.999   ,       lowercase_       :  float = 1E-8   ,       lowercase_       :  Optional[float] = None   ,       lowercase_       :  Optional[float] = None   ,       lowercase_       :  float = 0.0   ,       lowercase_       :  float = 1.0   ,       lowercase_       :  Optional[List[str]] = None   ,       ):
       """simple docstring"""




       a_						     =			tf.keras.optimizers.schedules.PolynomialDecay(
           initial_learning_rate=lowercase_   ,       decay_steps=num_train_steps - num_warmup_steps   ,       end_learning_rate=init_lr * min_lr_ratio   ,       power=lowercase_   ,       )
       if num_warmup_steps:
              a_						     =			WarmUp(
                  initial_learning_rate=lowercase_   ,       decay_schedule_fn=lowercase_   ,       warmup_steps=lowercase_   ,       )
       if weight_decay_rate > 0.0:
              a_						     =			AdamWeightDecay(
                  learning_rate=lowercase_   ,       weight_decay_rate=lowercase_   ,       beta_a=lowercase_   ,       beta_a=lowercase_   ,       epsilon=lowercase_   ,       clipnorm=lowercase_   ,       global_clipnorm=lowercase_   ,       exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']   ,       include_in_weight_decay=lowercase_   ,       )
       else:
              a_						     =			tf.keras.optimizers.Adam(
                  learning_rate=lowercase_   ,       beta_a=lowercase_   ,       beta_a=lowercase_   ,       epsilon=lowercase_   ,       clipnorm=lowercase_   ,       global_clipnorm=lowercase_   ,       )
       # We return the optimizer and the LR scheduler in order to better track the
       # evolution of the LR independently of the optimizer.
       return optimizer, lr_schedule




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__ = 0.001   ,	UpperCamelCase__ = 0.9   ,	UpperCamelCase__ = 0.999   ,	UpperCamelCase__ = 1e-7   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = 0.0   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = "AdamWeightDecay"   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__	)
         a_						     =			weight_decay_rate
         a_						     =			include_in_weight_decay
         a_						     =			exclude_from_weight_decay







  @classmethod
  def    _a       (			cls   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			{'WarmUp': WarmUp}
         return super(UpperCamelCase__   ,	cls	).from_config(UpperCamelCase__   ,	custom_objects=UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         super(UpperCamelCase__   ,	self	)._prepare_local(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)
         a_						     =			tf.constant(
             self.weight_decay_rate   ,	name='adam_weight_decay_rate'	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self._do_use_weight_decay(var.name	)
         if do_decay:
                return var.assign_sub(
                    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']   ,	use_locking=self._use_locking   ,	)
         return tf.no_op()







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_     ,	a_						     =			list(zip(*UpperCamelCase__	)	)
         return super(UpperCamelCase__   ,	self	).apply_gradients(zip(UpperCamelCase__   ,	UpperCamelCase__	)   ,	name=UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         if apply_state is None:
                return self._decayed_lr_t[var_dtype], {}

         a_						     =			apply_state or {}
         a_						     =			apply_state.get((var_device, var_dtype)	)
         if coefficients is None:
                a_						     =			self._fallback_apply_state(UpperCamelCase__   ,	UpperCamelCase__	)
                a_						     =			coefficients

         return coefficients["lr_t"], {"apply_state": apply_state}







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=None	):






         """simple docstring"""


         a_     ,	a_						     =			self._get_lr(var.device   ,	var.dtype.base_dtype   ,	UpperCamelCase__	)
         a_						     =			self._decay_weights_op(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)
         with tf.control_dependencies([decay]	):
                return super(UpperCamelCase__   ,	self	)._resource_apply_dense(UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=None	):






         """simple docstring"""


         a_     ,	a_						     =			self._get_lr(var.device   ,	var.dtype.base_dtype   ,	UpperCamelCase__	)
         a_						     =			self._decay_weights_op(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)
         with tf.control_dependencies([decay]	):
                return super(UpperCamelCase__   ,	self	)._resource_apply_sparse(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}	)
         return config







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if self.weight_decay_rate == 0:
                return False

         if self._include_in_weight_decay:
                for r in self._include_in_weight_decay:
                       if re.search(UpperCamelCase__   ,	UpperCamelCase__	) is not None:
                              return True

         if self._exclude_from_weight_decay:
                for r in self._exclude_from_weight_decay:
                       if re.search(UpperCamelCase__   ,	UpperCamelCase__	) is not None:
                              return False
         return True





class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self	):






         """simple docstring"""


         a_						     =			[]
         a_						     =			None







  @property
  def    _a       (			self	):






         """simple docstring"""


         if self._accum_steps is None:
                a_						     =			tf.Variable(
                    tf.constant(0   ,	dtype=tf.intaa	)   ,	trainable=UpperCamelCase__   ,	synchronization=tf.VariableSynchronization.ON_READ   ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,	)

         return self._accum_steps.value()







  @property
  def    _a       (			self	):






         """simple docstring"""


         if not self._gradients:
                raise ValueError('The accumulator should be called first to initialize the gradients'	)
         return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]







  def __call__(			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if not self._gradients:
                a_						     =			self.step  # Create the step variable.
                self._gradients.extend(
                    [
                        tf.Variable(
                            tf.zeros_like(UpperCamelCase__	)   ,	trainable=UpperCamelCase__   ,	synchronization=tf.VariableSynchronization.ON_READ   ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,	)
                        if gradient is not None
                        else gradient
                        for gradient in gradients
                    ]	)
         if len(UpperCamelCase__	) != len(self._gradients	):
                raise ValueError(f'Expected {len(self._gradients	)} gradients, but got {len(UpperCamelCase__	)}'	)

         for accum_gradient, gradient in zip(self._gradients   ,	UpperCamelCase__	):
                if accum_gradient is not None and gradient is not None:
                       accum_gradient.assign_add(UpperCamelCase__	)

         self._accum_steps.assign_add(1	)







  def    _a       (			self	):






         """simple docstring"""


         if not self._gradients:
                return
         self._accum_steps.assign(0	)
         for gradient in self._gradients:
                if gradient is not None:
                       gradient.assign(tf.zeros_like(UpperCamelCase__	)	)












'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__lowerCAmelCase        =							logging.get_logger(__name__)


__lowerCAmelCase        =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase        =							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowerCAmelCase        =							{"facebook/blenderbot-3B": 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			(
           list(range(ord('!'   )   ,       ord('~'   ) + 1   )   ) + list(range(ord('¡'   )   ,       ord('¬'   ) + 1   )   ) + list(range(ord('®'   )   ,       ord('ÿ'   ) + 1   )   )
       )
       a_						     =			bs[:]
       a_						     =			0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowercase_   )
                     cs.append(2**8 + n   )
                     n += 1
       a_						     =			[chr(lowercase_   ) for n in cs]
       return dict(zip(lowercase_   ,       lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			set()
       a_						     =			word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              a_						     =			char
       return pairs




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : Union[str, Any]      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Optional[int]      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : str      			=     ['''input_ids''', '''attention_mask''']







  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__="replace"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=False   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else bos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else eos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else sep_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else cls_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else unk_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else mask_token

         super().__init__(
             errors=UpperCamelCase__   ,	bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	add_prefix_space=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         with open(UpperCamelCase__   ,	encoding='utf-8'	) as vocab_handle:
                a_						     =			json.load(UpperCamelCase__	)
         a_						     =			{v: k for k, v in self.encoder.items()}
         a_						     =			errors  # how to handle errors in decoding
         a_						     =			bytes_to_unicode()
         a_						     =			{v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase__   ,	encoding='utf-8'	) as merges_handle:
                a_						     =			merges_handle.read().split('\n'	)[1:-1]
         a_						     =			[tuple(merge.split()	) for merge in bpe_merges]
         a_						     =			dict(zip(UpperCamelCase__   ,	range(len(UpperCamelCase__	)	)	)	)
         a_						     =			{}
         a_						     =			add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         a_						     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def    _a       (			self	):






         """simple docstring"""


         return len(self.encoder	)







  def    _a       (			self	):






         """simple docstring"""


         return dict(self.encoder   ,	**self.added_tokens_encoder	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if token in self.cache:
                return self.cache[token]
         a_						     =			tuple(UpperCamelCase__	)
         a_						     =			get_pairs(UpperCamelCase__	)

         if not pairs:
                return token

         while True:
                a_						     =			min(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: self.bpe_ranks.get(UpperCamelCase__   ,	float('inf'	)	)	)
                if bigram not in self.bpe_ranks:
                       break
                a_     ,	a_						     =			bigram
                a_						     =			[]
                a_						     =			0
                while i < len(UpperCamelCase__	):
                       try:
                              a_						     =			word.index(UpperCamelCase__   ,	UpperCamelCase__	)
                       except ValueError:
                              new_word.extend(word[i:]	)
                              break
                       else:
                              new_word.extend(word[i:j]	)
                              a_						     =			j

                       if word[i] == first and i < len(UpperCamelCase__	) - 1 and word[i + 1] == second:
                              new_word.append(first + second	)
                              i += 2
                       else:
                              new_word.append(word[i]	)
                              i += 1
                a_						     =			tuple(UpperCamelCase__	)
                a_						     =			new_word
                if len(UpperCamelCase__	) == 1:
                       break
                else:
                       a_						     =			get_pairs(UpperCamelCase__	)
         a_						     =			' '.join(UpperCamelCase__	)
         a_						     =			word
         return word







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for token in re.findall(self.pat   ,	UpperCamelCase__	):
                a_						     =			''.join(
                    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__	).split(' '	)	)
         return bpe_tokens







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.encoder.get(UpperCamelCase__   ,	self.encoder.get(self.unk_token	)	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.decoder.get(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			''.join(UpperCamelCase__	)
         a_						     =			bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'   ,	errors=self.errors	)
         return text







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as f:
                f.write(json.dumps(self.encoder   ,	indent=2   ,	sort_keys=UpperCamelCase__   ,	ensure_ascii=UpperCamelCase__	) + '\n'	)

         a_						     =			0
         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as writer:
                writer.write('#version: 0.2\n'	)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,	key=lambda UpperCamelCase__	: kv[1]	):
                       if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                  ' Please check that the tokenizer is not corrupted!'	)
                              a_						     =			token_index
                       writer.write(' '.join(UpperCamelCase__	) + '\n'	)
                       index += 1

         return vocab_file, merge_file







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False	):






         """simple docstring"""


         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=UpperCamelCase__   ,	token_ids_a=UpperCamelCase__   ,	already_has_special_tokens=UpperCamelCase__	)

         if token_ids_a is None:
                return [1] + ([0] * len(UpperCamelCase__	)) + [1]
         return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.pop('add_prefix_space'   ,	self.add_prefix_space	)
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__	) > 0 and not text[0].isspace()):
                a_						     =			' ' + text
         return (text, kwargs)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         return token_ids_a + [self.eos_token_id]







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(' ' + text	)
                else:
                       # Generated responses should contain them already.
                       inputs.append(UpperCamelCase__	)

         a_						     =			'  '.join(UpperCamelCase__	)
         a_						     =			self.encode(UpperCamelCase__	)
         if len(UpperCamelCase__	) > self.model_max_length:
                a_						     =			input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'	)
         return input_ids




